Update app.py

app.py

@@ -91,8 +91,8 @@ with gr.Blocks(theme=theme) as demo:
         ''')
     gr.Markdown(model_card)
     gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py\">scikit-learn</a>")
-    alpha = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Constant that multiplies the L1 term, controlling regularization strength. Using alpha = 0 with the Lasso object is not advised")
-    l1_ratio = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label="The ElasticNet mixing parameter. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.")
+    alpha = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Controlling regularization strength: alpha. Using alpha = 0 with the Lasso object is not advised")
+    l1_ratio = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label="The ElasticNet mixing parameter: l1_ratio. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.")
     n_samples = gr.Slider(minimum=50, maximum=500, step=50, value=50, label="Number of samples")
     n_features = gr.Slider(minimum=50, maximum=200, step=50, value=50, label="Number of features")
     with gr.Row():